\chapter{Conclusion and Future work}

\subsection{Conclusion}

The problem of retrieving an image that matches the semantics of a text document is difficult. We are trying to solve this problem by extracting important keywords from the text document and using keywords for retrieving a relevant image. 

In this report, we discussed our overall system and existing work on keyword extraction based on unsupervised and supervised approaches. Our experiments on keyword extraction based on \textit{Counting and Proximity}, \textit{Modified TextRank}, \textit{Naive Bayes} and \textit{HMM} are discussed with results.


Future plans are discussed in the next section.

\section{Future Plan}

\subsection{Short term}
Short term plan is to complete the tagging of images for \textit{Representative images}. \textit{Representative images} are the one, which are easy compared to other types. 

\subsection{Mid term}

Mid term goal is to complete fully working system. Further investigation on possibility of using external resources apart from a given news article and descriptive meta data.
\begin{enumerate}
\item{Utilizing the click through logs and query words.}
\item{Utilizing the semantic data from Freebase, or others.}
\item{Clustering as approach to make a bottom-line of current news article based articles appeared previous day or week.}
\item{Feedback based learning }
\end{enumerate}


\subsection{Long term}

Long term goal is deep understanding of text and images and moving beyond keyword extraction. One possible extension of this problem may be matching sentiments of both representation for retriving images.



